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Lifecycle support— including continuous development, training, testing, and deployment of machine learning models—and continuous integration (CI) for AI applications is still in its infancy. We need a solution that enables end-to-end automation of data preparation and model deployment pipelines.

In this talk we are going to show how to leverage KNative components to create an event driven AI pipeline. We will leverage OpenWhisk and Kubernetes to provide an event driven platform, and Istio for traffic management and observability to construct a pipeline which will provide interfaces to various open source tools: model training, validation. serving platforms on Kubernetes

We will show how we can leverage this AI pipeline to train using advanced batch scheduling in Kubernetes, automate A/B tests and canary testing of models, monitoring concept drifts and accuracy losses etc.

Tommy Li is a software developer in IBM focusing on Cloud, Kubernetes, and Machine Learning. He is one of the Fabric for Deep Learning’s main contributors and worked on various developer code patterns on Kubernetes, Microservice, and deep learning application to provide use cases... Read More →

Animesh Singh is an STSM and lead for CODAIT and works with IBM Watson and Cloud Platform, where he leads machine learning and deep learning initiatives and works with communities and customers to design and implement deep learning, machine learning, and cloud computing frameworks... Read More →